Data science specialist: position is trend in the Logistics sector

According to the Future of Work 2025 report, carried out by the World Economic Forum, Brazilian employers predict that the functions of specialist in Digital Transformation, in AI and Machine Learning and in Supply Chain logistics will grow until 2030. 

This growth fills a major gap in the Logistics and Supply Chain Management sector: the lack of technical skills to implement data science, which has stood out as an essential competency for the sector. 

With the increasing reliance on decisions based on accurate information to improve efficiency, it becomes imperative to invest in internal talent, or hire employees who know how to apply good practices of integration, processing and data analysis. 

To make an overview, data science allows a detailed view of information throughout all stages of the logistics chain. Advanced analytical tools bring numerous benefits: from the in-depth analysis of data, companies can predict demands, manage inventories and optimize routes, and reduce waste.  

With these analyses, it is also possible to identify hidden patterns, anomalies and trends, allowing companies to anticipate potential problems and bottlenecks.These practices not only increase operational efficiency, but also ensure fast and accurate responses to market changes and internal needs.  

Operational research, in turn, uses advanced methods to solve complex problems and optimize resource allocation. Its applications range from choosing the ideal location for distribution centers to defining routes and optimal inventory levels. This approach also allows you to simulate scenarios and evaluate the impact of different decisions before implementing them, minimizing risks and maximizing efficiency.  

In an increasingly competitive environment, mastering these operational research techniques is a strategic differentiator for industry professionals.At the same time, the ability to transform large volumes of data into applicable insights makes data science an essential skill for modern logistics and supply chain management.  

Challenges along the way 

While promising, these areas are still relatively new, and one of the biggest challenges is the integration between old IT systems and new data science technologies.Many companies still use tools incompatible with modern solutions, making it difficult to collect and integrate relevant data.  

Another challenge is cultural resistance to data-driven decisions.Many professionals still prefer to rely on experience and intuition, which requires organizational change that starts from leadership, promoting the appreciation of evidence-based decisions.In addition, the quality and integrity of data are critical to avoid analysis errors that can lead to misguided decisions, requiring robust governance processes to ensure accurate, complete and consistent information.  

Despite these difficulties, obstacles can be overcome with investments in technology, training and cultural change. Data science and operational research are essential skills for modern logistics, not only by optimizing efficiency, but also by offering a strategic view of the business. Companies that exploit the full potential of these disciplines will be better positioned at the forefront of innovation and better prepared to compete in the market.